CEP Controle Estatístico de Processo, conceitos de controles aula 1

Summary

This video introduces Statistical Process Control (SPC), exploring how to measure and control process variation. It covers the history of SPC, starting with Walter Shewhart's development of the theory in 1924. The lesson explains two types of process variation: common cause and special cause, emphasizing the importance of control charts for detecting and addressing special causes. The video also discusses SPC's evolution and its practical application in improving processes, preparing viewers for a deeper dive into control charts and other SPC tools in upcoming lessons.

Takeaways

• 😀 The course introduces Statistical Process Control (SPC) and its importance in measuring and controlling process variation.
• 😀 The course will compare Run Charts with Control Charts and explain how to interpret them.
• 😀 SPC was developed by Walter Shewhart in 1924, who is considered the father of Statistical Process Control.
• 😀 Shewhart's theory of SPC includes two types of variation: common cause variation and special cause variation.
• 😀 Common cause variation is inherent in the process, random, and generally undetectable.
• 😀 Special cause variation happens intermittently and can be detected and removed from the process.
• 😀 Understanding the two types of variation helps in analyzing process characteristics and estimating parameters such as mean and standard deviation.
• 😀 Changes in process variation can be tracked over time by plotting samples on control charts.
• 😀 Control charts help in identifying special causes of variation, which can be corrected to improve the process.
• 😀 The course will review the history of SPC and explore the contributions of other quality experts like Edward Deming, Juran, and Crosby.
• 😀 The PDSA (Plan-Do-Study-Act) cycle, often confused with PDCA, will be discussed in further detail in later lessons.

Q & A

What is the primary objective of the course on Statistical Process Control?

The primary objective is to describe how to use Control Charts (CP) to measure and control process variation, compare Run Charts and Control Charts, and explain the rules for interpreting the charts.

Who developed the theory of Statistical Process Control (SPC)?

The theory of Statistical Process Control was developed by Walter Schuahrt in 1924.

What did Schuahrt publish in the 1930s regarding SPC?

In the 1930s, Schuahrt presented his theory and published it in his book, which contributed to the widespread application of SPC in businesses during the 1940s.

What are the two main types of variation in a process according to Schuahrt's theory?

According to Schuahrt's theory, there are two main types of variation: variation due to common causes and variation due to special causes.

What are 'common cause variations'?

Common cause variations are inherent to the process itself. These variations are random and are not detectable within the process.

What are 'special cause variations'?

Special cause variations are variations that occur intermittently within the process. These causes are detectable and removable from the process.

What is the role of Control Charts in Statistical Process Control?

Control Charts are used to plot data over time, revealing changes in the process and identifying special causes of variation that can help improve the process.

How can professionals use Control Charts to improve a process?

Professionals can use Control Charts to evaluate process variation, identify special causes, and take corrective actions to improve the process.

What is the PDSA cycle and how is it related to Statistical Process Control?

The PDSA (Plan-Do-Study-Act) cycle is a fundamental concept in SPC. It is used for continuous improvement of processes, and while it is similar to the PDCA (Plan-Do-Check-Act) cycle, it focuses more on studying the results of actions taken.

Who are some of the other quality gurus mentioned in the transcript besides Schuahrt?

Other quality gurus mentioned are Edward Deming, Juran, and Crosby, who contributed to the development and application of quality management principles.